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Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

Abstract : In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.
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https://hal.inria.fr/inria-00542496
Contributor : Salvatore Tabbone Connect in order to contact the contributor
Submitted on : Thursday, December 2, 2010 - 5:12:00 PM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM

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Nafaa Nacereddine, Salvatore Tabbone, Djemel Ziou, Latifa Hamami. Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation. 20th International Conference on Pattern Recognition - ICPR 2010, IAPR, Aug 2010, Istanbul, Turkey. pp.4557 - 4560, ⟨10.1109/ICPR.2010.1107⟩. ⟨inria-00542496⟩

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